Predictive Handover via Sensor Fusion and Kalman Filtering
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing handover decision methods in multi-cell wireless networks rely primarily on single scalar values like RSSI, which are unreliable due to influences such as radio fades and multipath reflections, leading to suboptimal handover decisions and potential dropped calls.
Innovation Solution
A system that uses a combination of sensors to gather data on the state of the wireless communication device and its environment, including position, velocity, and signal strengths, and employs estimation and filtering methods like Kalman and particle filters to generate accurate estimates of the device's state, enabling predictive handover decisions based on projected positions and velocities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If handover decision is made based on single scalar value (RSSI), then the decision process is simple, but the reliability of handover decision deteriorates due to radio fades and multipath reflections
Solution Approach 1:
The patent segments the handover decision process by separating the measurement function (performed by mobile device using multiple sensors to collect RSSI, channel quality, packet loss data) from the decision function (performed by network controller that processes segmented measurement data and generates handover commands). This segmentation allows complex multi-parameter analysis at the network level while keeping mobile device complexity low.
Solution Approach 2:
The network controller acts as an intermediary between mobile devices and the core network. It collects measurement reports from multiple mobile devices, processes the data using filtering algorithms to eliminate false readings from radio fades and multipath reflections, and makes centralized handover decisions. This intermediary approach improves reliability by pooling data from multiple sources and applying sophisticated processing that individual mobile devices cannot perform.
2Ease of operation
If handover decision is made using reactive indicators (RSSI, packet loss), then the implementation is straightforward, but the accuracy of handover timing deteriorates due to lagging indicators
Solution Approach 1:
The network controller performs preliminary analysis of measurement trends and predicts future channel conditions before actual handover is needed. By analyzing the direction and rate of change of RSSI and packet loss indicators, the system anticipates degradation and triggers handover in advance, converting reactive indicators into predictive decisions. This preliminary action prevents dropped calls by acting before signal quality completely deteriorates.
Solution Approach 2:
The system implements continuous feedback loops where mobile devices send periodic measurement reports to the network controller, which processes the data and adjusts handover decisions based on observed trends. The feedback mechanism allows the system to learn from past handover outcomes and refine its predictions, improving timing accuracy while maintaining straightforward implementation through standardized measurement reporting procedures.
3Reliability
If multiple sensors and estimation methods are used to improve state estimation accuracy, then the reliability of communication improves, but the device complexity increases
Solution Approach 1:
The patent divides the system into two complexity zones: the mobile device contains only simple sensors (accelerometer, gyroscope, magnetometer, GPS) and basic data collection functions, while the complex estimation and filtering algorithms (Kalman filters, particle filters) are executed on the network controller or server. This segmentation allows high reliability through sophisticated processing while keeping mobile device complexity low and power consumption acceptable.
Solution Approach 2:
The network controller serves as an intermediary that receives raw sensor data from mobile devices and performs the computationally intensive tasks of multi-sensor fusion and state estimation. This intermediary approach allows mobile devices to benefit from high-accuracy positioning and handover decisions without bearing the computational burden, thus improving communication reliability while maintaining device simplicity.
Data Source
AI summary
A system (10) for implementing an action on or associated with a wireless communication network (26), the system comprising: a wireless communication enabled device (24) operable to communicate with the wireless communication network; and at least one sensor (50) for sensing and gathering sensor data relating to a state of the wireless communication enabled device or a state of the environment surrounding the wireless communication enabled device, wherein the wireless communication enabled device comprises an estimation means (62) for receiving the sensor data and estimating the state of the wireless communication enabled device based on the sensor data, and a decision means (64) for determining whether to implement the action based on the estimated state of the wireless communication enabled device.